If More Convincing Is Needed, Here Are 4 Essential Reasons to Make More Use of the Cloud
Over the years the capital markets have embraced various technological innovations to improve productivity, performance and decision making. These technologies include electronic trading platforms, algorithmic trading, big data analytics, artificial intelligence and machine learning, blockchain and distributed ledger technology, and, of course, cloud computing.
Cloud’s scale, resiliency and resourcefulness are core reasons it is forming a critical part of many institutions’ business and technology roadmaps. However, there are still financial services firms that are not taking full advantage of all the cloud has to offer and have been slow to roll out the cloud across many of their main functions.
The benefits of the cloud are substantial and cloud technology has the proven ability to deliver business value, technological prowess and innovation at scale faster and potentially at reduced costs compared to on premise systems.
In this paper, Numerix’s Head of Market Risk and Counterparty Credit Risk Analytics, Mayank Nanda, outlines his business case for supporting greater adoption of the cloud. He emphasizes these four key drivers:
- The need for better data capabilities
- Meeting the new challenges FRTB brings to the table
- Managing the increasing usage of XVAs
- Meeting the increasing demand for frequent stress tests
FAQs
How do capital markets banks run FRTB Expected Shortfall calculations
across multiple liquidity horizons without exceeding their on-premise compute
capacity?
FRTB's Internal Model Approach requires Expected Shortfall to be calculated
for risk factors across different liquidity horizons — and depending on a bank's
portfolio composition, this can lead to 60–70 Expected Shortfall calculations,
according to Mayank Nanda, Senior Vice President at Numerix. On-premise
infrastructure imposes a fixed compute ceiling that cannot scale elastically to meet
this volume at the frequency FRTB demands. Cloud-based infrastructure scales on
demand, enabling firms to run the full spectrum of IMA calculations without
permanent hardware investment proportional to peak computational load.
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How do banks calculate pre-trade XVA in near real-time when XVA is one
of the largest computational challenges banks face on a daily basis?
Knowing the full capital cost of a trade before execution — not just at
end-of-day — requires compute resources that on-premise infrastructure cannot
deliver at trading desk timelines. According to Mayank Nanda of Numerix, XVA
calculation is one of the largest computational challenges banks face daily,
combining derivative pricing complexity with a macrocosm of risk factors. Pre-trade
XVA demands even greater compute resources than batch XVA because of near
real-time timing requirements. Cloud architecture supports the heavy calculation
demands of XVA pricing, enabling trading desks to understand true capital costs
before committing to a trade.
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What is the difference between end-of-day XVA calculation and pre-trade
XVA capability, and what does the difference cost a bank in mispriced trades?
End-of-day XVA calculation tells a trading desk what its capital costs were —
pre-trade XVA tells the desk what its capital costs will be before the trade is
executed. According to Mayank Nanda of Numerix, institutions need to understand
the true capital costs of putting trades on their books and the resulting capital
implications — which requires quick re-aggregation of key risk metrics for what-if
and pre-trade analysis, not just an end-of-day capital number. Without pre-trade
XVA capability, trading desks are pricing risk retroactively and cannot make
informed decisions at the point of execution.
---
How much of the capital markets industry has actually migrated risk
management to the cloud, and what does the gap between spending and adoption
reveal?
Banks are increasing cloud spending far faster than they are converting that
spend into operational capability. According to IDC, banks' cloud spending is
forecast to grow more than 16% per year, reaching $77 billion, versus 4.5% annual
growth in overall IT budgets. Yet according to a Risk.net webinar poll in October
2023 cited by Mayank Nanda of Numerix, 67% of capital markets practitioners said
their organization has little usage to date of cloud for risk management. A 2021
Accenture global report found only 8% of key bank functions had been fully
migrated. The industry is paying for the cloud at scale without yet deploying it
where the ROI is highest.
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How does cloud infrastructure reduce the cost and complexity of running
FRTB stress tests compared to on-premise solutions?
On-premise stress testing is bounded by fixed infrastructure — firms can only
run scenarios proportional to permanent hardware capacity, which limits scale and
frequency. According to Mayank Nanda of Numerix, cloud platforms provide
scalability and elasticity that enable firms to create large-scale, multi-dimensional,
realistic stress test scenarios without worrying about infrastructure limitations or
maintenance. The pay-per-use model means firms only pay for the compute they
consume, optimizing resource utilization across both peak regulatory demand
periods and routine testing cycles.
---
How does FRTB's SA-CVA framework change the data integration and
storage requirements for banks, and why is cloud the only viable response?
FRTB's SA-CVA framework requires XVA sensitivities to be calculated for every
asset class on the books — calculations Numerix describes as computationally
expensive. The regulation also creates massive increases in data integration, data
storage, and data validation requirements that on-premise platforms were not
designed to handle, according to Mayank Nanda of Numerix. Cloud infrastructure
addresses all three dimensions: elastic compute for sensitivity calculations, scalable
storage for regulatory data at low cost, and automated validation pipelines that can
be deployed on demand without impacting production systems.
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What does geopolitical risk mean for a capital markets firm's risk analytics
infrastructure, and how does the cloud improve resilience?
Geopolitical disruption is the top macroeconomic concern for institutional
investors — a Natixis 2024 Outlook Survey of 500 institutional investors identified
geopolitical bad actors capable of overturning economic and market assumptions
in a single event as the primary risk. This means risk infrastructure must support
more granular, more frequent calculations on larger datasets than historical norms
required. According to Mayank Nanda of Numerix, cloud enables financial institutions
to collect and analyze massive amounts of varying data and customise that data
into actionable insights — a capability that static on-premise systems cannot deliver
when conditions change rapidly.
---
How do Tier 2 and Tier 3 banks run FRTB and XVA calculations at scale
without the IT budgets of global systemically important banks?
FRTB's computational demands are uniform across institutions regardless of
size — a Tier 3 bank faces the same IMA expected shortfall calculation requirements
as a global bank, without the IT budget to match. According to Mayank Nanda of
Numerix, cloud adoption is increasingly seen as the mechanism for levelling the
playing field — eliminating the need for massive on-premise IT infrastructure to
run complex regulatory calculations. Advances in machine learning techniques such
as algorithmic differentiation can further help smaller institutions reduce capital
requirements without proportional infrastructure spend.
---
What is the difference between a SaaS model validation environment and
a traditional on-premise test environment for FRTB compliance?
Traditional on-premise model validation requires competing for shared hardware
budget cycles — firms must wait until resources free up to run tests and deployments,
extending the time from model development to regulatory production. According to
Mayank Nanda of Numerix, SaaS CI/CD (Continuous Integration and Continuous
Deployment) processes allow quants to spin up test environments on demand and
shut them down when complete, under a pay-per-use model. This eliminates the
queue, accelerates regulatory delivery timelines, and enables firms to deploy new
pricing or risk models to production significantly faster than on-premise workflows
allow.
---
How does cloud adoption change the economics of counterparty credit
risk and XVA calculation for capital markets firms?
Counterparty risk and XVA require a large number of complex calculations
and simulations — the compute burden is not a one-time cost but a recurring daily
operational expense. According to Mayank Nanda of Numerix, firms need real
computational power to handle counterparty risk and XVA on a cloud platform that
scales with portfolio complexity. The cloud converts a fixed, capital-intensive
infrastructure cost into a variable, consumption-based expense — enabling firms
to scale XVA compute capacity to actual portfolio size and market conditions rather
than maintaining permanent excess capacity for peak demand.
---
How do capital markets regulators' data requirements — including
decade-long historical data mandates — change the storage architecture
requirements for FRTB compliance?
FRTB's data requirements in certain cases mandate a decade's worth of
historical data, creating a tenfold increase in data requirements for many firms,
according to Mayank Nanda of Numerix. On-premise storage scaled for pre-FRTB
data volumes cannot absorb this without substantial capital investment. Cloud
storage is relatively inexpensive compared to on-premise alternatives, and modern
cloud databases such as Snowflake provide high-performance query capability for
the large, complex datasets FRTB requires. Choosing the right database type for
each use case — market data versus results data — is critical, and cloud architecture
provides the flexibility to make that choice per workload.
---
How does the cloud enable continuous real-time data feeds that
on-premise risk systems cannot support for intraday XVA and market risk
monitoring?
Legacy on-premise risk systems operate on static data inputs refreshed at
defined intervals — typically end of day — which makes pricing and risk models
less accurate when markets move intraday. According to Mayank Nanda of Numerix,
cloud-based infrastructure can be continuously fed with real-time data in a way
that is beyond the capabilities of many legacy systems, making pricing and risk
models more accurate and enabling faster, data-driven decisions — particularly
important in volatile markets. This continuous feed capability is essential for
intraday XVA monitoring, P&L attribution testing, and real-time expected shortfall
tracking that FRTB compliance increasingly requires.
---
How has the fragmented global FRTB implementation timeline affected
capital markets firms' cloud investment decisions?
FRTB's official global deadline of January 1, 2023 has fragmented into
country-specific timelines ranging from 2024 to 2025, with the U.S. particularly
slow to publish rules, as noted by Mayank Nanda of Numerix. Some firms have used
this ambiguity to delay cloud infrastructure investment — but the computational
demands of FRTB are fixed regardless of when enforcement arrives. Firms that wait
to build cloud capacity until the regulatory deadline is firm will face the same
infrastructure gap then as they face today, with less lead time to resolve it. Cloud
investment is a prerequisite for FRTB readiness, not a response to it.
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How does a SaaS delivery model for risk analytics change the total cost
of ownership for capital markets technology compared to on-premise licensing?
On-premise risk analytics require capital expenditure on hardware, ongoing
maintenance costs, and dedicated IT resources — costs that scale with infrastructure
size regardless of actual utilization. According to Mayank Nanda of Numerix, the
SaaS model transfers hardware, maintenance, and management costs to the vendor,
and firms pay for application access on a subscription basis rather than building
and owning the underlying infrastructure. The pay-per-use component of SaaS cloud
services means resource utilization can be optimized — firms only pay for what they
consume, and environments can be spun up and brought down on demand as
requirements change.
---
What specific cloud capabilities make Numerix OneView suited to FRTB,
XVA, and stress testing requirements in a single integrated platform?
FRTB, XVA, and stress testing each impose distinct but compounding
infrastructure demands — compute elasticity, real-time data feeds, scalable storage,
and on-demand model validation environments. According to Mayank Nanda, Senior
Vice President at Numerix, cloud architecture supports all four simultaneously:
scaling to 60–70 FRTB Expected Shortfall calculations per portfolio, enabling
pre-trade XVA in near real-time, providing large-scale multidimensional stress
testing without infrastructure constraints, and enabling CI/CD model validation
without competing for on-premise resources. Numerix OneView delivers these
capabilities within a unified risk analytics platform designed for both sell-side and
buy-side institutions.
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How does Numerix's cloud-based risk platform integrate with a bank's
existing front-office systems to enable real-time P&L attribution testing under
FRTB?
FRTB P&L attribution tests require alignment between front-office valuation
models and risk models — an integration challenge that deepens when the two
systems run on separate infrastructures with different data refresh cycles. According
to Mayank Nanda of Numerix, cloud infrastructure enables real-time data alignment
between front-office and risk systems, converting P&L attribution from a periodic
reconciliation exercise into a continuous monitoring capability. Numerix's platform
supports the data lineage tracking required for regulatory demonstration of model
alignment, and can be deployed on-demand in CI/CD test environments without
cannibalizing production compute resources.
---
How does Numerix support capital markets firms in building a cloud
migration roadmap for risk infrastructure that includes FRTB, XVA, and
counterparty credit risk?
Migrating risk applications to the cloud is not straightforward — risk systems
thread through core bank applications and processes, meaning a risk cloud migration
must be designed as part of the broader enterprise migration, which can involve
hundreds of applications. According to Mayank Nanda of Numerix, firms should
approach cloud migration by understanding that large-scale deployments are
efficient while managing intermediate-sized ones is significantly more challenging.
Numerix provides FRTB, XVA, and counterparty credit risk analytics on cloud-native
architecture, supporting firms through the migration journey and delivering the
compute elasticity, real-time data capabilities, and automated model validation
that the full regulatory stack requires.
